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PLP 6404

Epidemiology of Plant Diseases

Spring 2015

Ariena van Bruggen, modified from Katherine Stevenson

Lecture 26: Crop loss assessment

Yield levels and losses

Theoretical yield

The yield obtained under the best growing conditions according to calculations

based on the plant and crop physiology or the maximum theoretical yield as

determined by using crop growth simulation models

Attainable yield

The yield achieved when crops are grown using all available pest control

technologies to minimize biotic stress

Economic yield

The level of yield where the value of the yield just exceeds the cost of production

Actual yield

The yield obtained under current agronomic husbandry practices

Primitive yield

The yield obtained without pest control (e.g. subsistence agriculture)

Introduction to crop loss

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The prevention of crop losses is the main reason that we study plant pathology. Zadoks & Schein

(1979) considered crop losses at the end point of epidemiology; this is where the science merges

with crop physiology, economics, and sociology.

Definitions:

Crop damage -- injuries caused by harmful biotic or abiotic agents that collectively

result in a measurable loss of yield

Crop loss -- a reduction in value or financial return due to damage; often measured

as the difference between actual yield and attainable yield due to pests

and/or pathogens

The reduction in value may be the result of

1. lower quantity

2. lower quality

Reliable yield loss data are always in demand (and difficult to get) and are used to:

determine research priorities

serve as a base of information for governmental decisions (local, regional or international

levels)

Potential and actual losses

A loss that may occur in the absence of any management practice is a potential loss.

A loss that has occurred (or is still occurring) is an actual loss.

Actual losses can be direct or indirect:

Losses in quality and quantity of product sustained by the grower, including costs of

pest management are referred to as direct losses.

Losses beyond the farm gate that arise as a consequence of plant pathogens or pests

are considered indirect losses (to community, consumer, wholesaler etc.).

Direct losses can be primary or secondary:

Preharvest and postharvest losses of plant products due to plant disease and pests,

including direct costs of pest management are primary losses.

Losses caused by a reduction in the yielding capacity of future crops sustained at the

grower level are considered secondary losses.

The crop loss estimates can be used to:

1. establish priorities for research programs;

2. justify programs on certain crops or problems;

3. predict yield and provide crop information;

4. plan strategies for future seasons, and;

5. compare seasons, epidemics, pathogens, etc.

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Measuring disease and yield in the field

Four basic approaches have been employed in crop loss studies:

1. single plant or tiller method

disease is assessed on a large number of individual plants (50-2000), covering the entire

range of possible disease intensity values.

yield of the individual plants is then measured and related to disease

problems include high inter-plant variability and low R2 values

2. microplot experiments

used primarily for soilborne pathogens

allows containment of pathogens and creation of wide range of disease levels

very labor intensive

3. Survey method

Involves measurement of many variables concerning biological and physical properties of

the crop and field, including disease intensity, usually at multiple times during the

growing season

4. conventional field experiments with different levels of disease

most widely used technique

should repeat experiment at least 3 years

A range of disease levels can be created by:

Add or remove inoculum

Timing or amount of fungicides applied

If possible, use "typical" farming equipment and practices.

Drawbacks to this approach:

usually includes effects of only a single pathogen - not very realistic

Note: The # of levels of disease intensity is more important than # of replications.

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Models for relating disease intensity and yield loss

Four categories of empirical loss models (what is an empirical model?):

single-point models integral models

multiple-point models generalized or nonlinear models

Single-point models

Single-point models relate yield loss to disease intensity at one specific time during the

growing seasons or at a specific host growth stage.

Most commonly used models; need relatively little data.

May be either linear or non-linear

Can be written in the form: L = a + bX, where L=loss and X=disease intensity

Example: Linear models of yield loss in soybeans as a function of incidence of

Sclerotinia stem rot (Yang, X. B., et al. 1999. Plant Disease 83:456-461)

(3 different years and 3 different locations)

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Example: Linear models of yield loss of chickpea as a function of intensity of fusarium

wilt (Navas-Cortes, J. A., et al., 2000. Phytopathology 90:1269-1278)

Incidence (I): 0=healthy, 1=diseased

Severity (S): 0=healthy, 1=1-33%, 2=34-66%, 3=67-100%, 4=dead plant

Disease intensity index (DII):

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Multiple-point or multiple regression models

Multiple-point models relate yield loss to assessments of disease made at several times

during a growing season.

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More applicable in situations where yield accumulation occurs over a relatively longtime

or rate of disease progress is highly variable.

Example: potato late blight and tuber yield (James, 1972)

L = 1.87X1 + 0.45X2 + 1.44X3 + 0.63X4 + 0.19X5 + 0.18X6 + 0.34X8 + 0.83X9

where L = % tuber yield loss and Xi = weekly change in % defoliation.

Difference between estimated loss and actual loss was less than 5% in 9 of 10

data sets, and has an R2 value of .952

Integral models

Yield or loss as a function of AUDPC

First used by Vanderplank for stem rust of wheat, estimation of yield loss is a function of

the area under the disease progress curve.

Problems with this approach: AUDPC doesn’t take into account

when disease started

that early disease may be more important

that it may not be a good predictor of yield

Weighting factors can be applied to disease assessments at particular times or

growth stages to account for the effect of time of disease occurrence on yield.

Example: Yield of soybean as a function of relative AUDPC (RAUDPC) for soybean

rust epidemics (Yang, X. B., et al., 1991. Phytopathology 81:1420-1426)

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Yield or loss as a function of healthy leaf area duration (HAD):

Waggoner and Berger (1987): Crop yield is determined not by the area of diseased

tissue, but by the area of productive, healthy tissue which is not reflected in

measurements of disease severity.

Yield loss is proportional to 1 - (HAD)/(LAD), where LAD is leaf area duration of a

disease-free control crop. (much more difficult to measure)

Example: Yield of peanut as a function of HAD and absorbed insolation. (Waggoner

and Berger, 1987. Phytopathology 77:393-398)

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Nonlinear models

more powerful than strictly linear modeling

account for more variation in response curve

need larger data sets to fit nonlinear models

Example: Relative yield of

chickpeas as a function of the

standardized areaunder the disease

intensity indexprogress curve

(Navas-Cortes, J. A., et al., 2000.

Phytopathology 90:1269-1278)

Negative exponential model:

RY = e1−erdx

RY = relative yield

x = AUDPC

rd = rate of decrease

(rd estimated from nonlinear

regression)

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Example: Seinhorst model (1965) for quantifying effects of nematodes on yield

Assessing and modeling losses in quality of yield

Losses in quality due to disease may be as important as losses in quantity.

For many products, such as fresh market fruits, quality grades are established and

prices received are determined by the grade.

Whenever quality is an important determinant of crop value, quality losses should be

considered in any investigation of the relationship between disease and loss.

Y = m+ (1-m)N (Z-t)

where Y = yield

m = minimum yield

t = threshold or

tolerance parameter

N = shape parameter

Z = nematode density

Effect of Meloidogyne

incognita on yield of fresh

spinach

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Example: Effect of apple scab on value of harvested fresh market apple fruit